
from data import Lattice, Catalogue
from utils import plotting
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly
plotly.offline.init_notebook_mode()


cat = Catalogue.from_file('./Unit_Cell_Catalog.txt', indexing=1)
print(cat)

Unit cell catalogue with 17222 entries


names = cat.names
for i in [0,2]:
    lat = Lattice(**cat.get_unit_cell(names[i]))
    fig = plotting.plotly_unit_cell_3d(lat)
    fig.show()


names = cat.names
lat = Lattice(**cat.get_unit_cell(names[1]))
fig = plotting.plotly_unit_cell_3d(lat)
fig.show()


df_data = {}
for name in tqdm(cat.names):
    lat = Lattice(**cat.get_unit_cell(name))
    lat.calculate_edge_lengths(repr='transformed')
    # extract minimum distance between any two nodes
    pos = lat.transformed_node_coordinates
    matrix = pos.reshape((lat.num_nodes, 1, 3)) - pos.reshape((1, lat.num_nodes, 3))
    distances = np.sqrt(np.sum(matrix**2, axis=2))
    distances = distances[np.triu_indices_from(distances, k=1)]
    props = {
        'num_nodes':lat.num_nodes,
        'num_edges':lat.num_edges,
        'min_edge_length':lat.transformed_edge_lengths.min(),
        'mean_edge_length':lat.transformed_edge_lengths.mean(),
        'max_edge_length':lat.transformed_edge_lengths.max(),
        'min_nodal_distance':distances.min()
    }
    df_data.update({name:props})
df = pd.DataFrame(df_data).T
df

100%|██████████| 17222/17222 [00:13<00:00, 1230.69it/s]


df.describe()


fig = make_subplots(
    rows=2, cols=2, 
    subplot_titles=("Number of nodes", "Number of edges", "Mean edge lengths", "Minimum nodal distance")
)
marker_dict = {'line':{'color':'black', 'width':0.5}}
fig.add_histogram(
    x=df['num_nodes'], name='Nodes', row=1, col=1, 
    marker=marker_dict
)
fig.add_histogram(
    x=df['num_edges'], name='Edges', row=1, col=2, 
    marker=marker_dict, 
)
fig.add_histogram(
    x=df['mean_edge_length'], name='Mean edge length', row=2, col=1, 
    marker=marker_dict, 
)
fig.add_histogram(
    x=df['min_nodal_distance'], name='Minimum distance between nodes', row=2, col=2, 
    marker=marker_dict, 
)
fig.update_layout(xaxis_range=[0,1000])
fig.update_layout(xaxis2_range=[0,1000])
fig.update_layout(title='Unit cell statistics')
fig.update_layout(height=800, width=800, showlegend=False)
fig


print(df[df.num_nodes==df.num_nodes.max()])

                 num_nodes  num_edges  min_edge_length  mean_edge_length  \
cub_Z04.0_R2526     4224.0     7008.0         0.000636          0.043935   

                 max_edge_length  min_nodal_distance  
cub_Z04.0_R2526         0.047514            0.000636


lat = Lattice(**cat.get_unit_cell('cub_Z04.0_R2526'))
fig = plotting.plotly_unit_cell_3d(lat, node_numbers=False)
fig.show()


print(df.sort_values(by='min_nodal_distance').head(10))

                  num_nodes  num_edges  min_edge_length  mean_edge_length  \
trig_Z05.0_E6999      136.0      143.0         0.000099          0.060219   
trig_Z05.9_E6124      113.0      132.0         0.028911          0.098573   
trig_Z04.8_E6128      128.0      132.0         0.002935          0.061820   
trig_Z05.1_E6998      132.0      143.0         0.022077          0.072384   
trig_Z04.5_E7306      103.0      118.0         0.000003          0.073825   
trig_Z04.4_E9350      116.0      150.0         0.000003          0.096554   
ort_Z05.2_E3240        44.0       56.0         0.000003          0.134318   
ort_Z05.0_E3485        46.0       59.0         0.000003          0.141029   
hex_Z03.5_R2193       124.0      152.0         0.000004          0.066356   
trig_Z05.3_E7254       80.0      121.0         0.000004          0.124587   

                  max_edge_length  min_nodal_distance  
trig_Z05.0_E6999         0.119700            0.000000  
trig_Z05.9_E6124         0.190920            0.000000  
trig_Z04.8_E6128         0.112510            0.000001  
trig_Z05.1_E6998         0.132310            0.000001  
trig_Z04.5_E7306         0.094420            0.000003  
trig_Z04.4_E9350         0.112409            0.000003  
ort_Z05.2_E3240          0.172888            0.000003  
ort_Z05.0_E3485          0.174143            0.000003  
hex_Z03.5_R2193          0.080096            0.000004  
trig_Z05.3_E7254         0.136151            0.000004


lat = Lattice(**cat.get_unit_cell('ort_Z05.2_E3240'))
fig = plotting.plotly_unit_cell_3d(lat, repr='transformed')
fig.show()

	num_nodes	num_edges	min_edge_length	mean_edge_length	max_edge_length	min_nodal_distance
cub_Z06.0_E1	8.0	12.0	1.000000	1.000000	1.000000	1.000000
ort_Z08.0_E2	8.0	12.0	0.850750	0.959102	1.026555	0.574490
cub_Z08.0_E3	9.0	8.0	0.866025	0.866025	0.866025	0.866025
hex_Z08.0_E4	8.0	14.0	0.994710	0.996221	1.000000	0.994710
tet_Z04.0_E5	12.0	14.0	0.500000	0.501286	0.503000	0.500000
...	...	...	...	...	...	...
tet_Z03.7_R2726	168.0	212.0	0.025296	0.071671	0.086391	0.025296
cub_Z03.5_R2727	96.0	132.0	0.209300	0.210274	0.211071	0.201681
cub_Z05.0_R2728	60.0	114.0	0.204120	0.249877	0.271540	0.204120
mon_Z05.7_R2729	34.0	45.0	0.000765	0.185892	0.241351	0.000765
cub_Z04.0_R2730	1160.0	2026.0	0.043030	0.084368	0.086736	0.043030

	num_nodes	num_edges	min_edge_length	mean_edge_length	max_edge_length	min_nodal_distance
count	17222.000000	17222.000000	17222.000000	17222.000000	17222.000000	17222.000000
mean	91.254268	113.180989	0.108358	0.234239	0.336314	0.092567
std	101.660985	150.516632	0.109203	0.110778	0.185275	0.098855
min	6.000000	6.000000	0.000003	0.031022	0.039054	0.000000
25%	40.000000	49.000000	0.024573	0.159061	0.204124	0.019207
50%	66.000000	80.000000	0.079647	0.215477	0.293243	0.065931
75%	112.000000	132.000000	0.153460	0.284022	0.420058	0.131330
max	4224.000000	7008.000000	1.000000	1.091532	1.410062	1.000000